Measurement apparatus

ABSTRACT

A measurement apparatus comprising at least one device interface adapted to connect an auxiliary measurement device and/or a device under test, DUT, to said measurement apparatus; a user interface adapted to input by a user settings for performing a measurement by said measurement apparatus and an artificial intelligence, AI, module adapted to provide current settings of said measurement apparatus, wherein said artificial intelligence, AI, module is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus.

TECHNICAL FIELD

The invention relates to a method and system for performing an automaticconfiguration or reconfiguration of a measurement apparatus, inparticular of a handheld test and measurement device using artificialintelligence.

TECHNICAL BACKGROUND

Measurement devices are used by technicians and operated mostly byrepeating certain measurements multiple of times or by usingstandardized measurement settings (e.g. so-called wizard sets). Thesemeasurement settings simplify measurements by automating, standardizingand optimizing test sequences. After a measurement sequence has beenconfigured by an expert, it can be transferred to measurementinstruments in the field. An operator working in the field only needs tostart the wizard set, select a measurement sequence and followpredefined instructions. A technician uses most of the time the samewizard sets in order to run tests or diagnostics on a wide range ofdevices under test DUTs. However, the effort to input the same settingsin multiple tests is quite big even when just using wizard sets,especially when the input of the settings has to be repeated formultiple devices under test DUTs. The repetitive use of certainmeasurement modes requires a higher effort for inputting settings orwizard sets. This increases significantly the time required forperforming measurements in the field.

Accordingly, there is a need to provide a method and apparatus whichincreases the efficiency for performing measurements and reduces therequired measurement time.

SUMMARY OF THE INVENTION

The invention provides according to a first aspect of the presentinvention a measurement apparatus comprising

-   -   at least one device interface adapted to connect an auxiliary        measurement device and/or a device under test to said        measurement apparatus,    -   a user interface adapted to input by a user settings for        performing a measurement by said measurement apparatus and an        artificial intelligence module adapted to provide current        settings of said measurement apparatus, wherein said artificial        intelligence module is machine learned on the basis of connected        devices and/or settings during historic measurements performed        by said measurement apparatus.

In a possible embodiment of the measurement apparatus according to thefirst aspect of the present invention, a measurement usage historyincluding connected devices and/or settings of measurements performed bysaid measurement apparatus is recorded over time in a memory.

In a possible embodiment of the measurement apparatus according to thefirst aspect of the present invention, the measurement usage history ofthe measurement apparatus is recorded in a local memory of saidmeasurement apparatus and/or in a remote database connectable to saidmeasurement apparatus.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the settings input by theuser via the user interface comprise measurement parameter settingsand/or measurement mode settings.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the machine learnedartificial intelligence module of the measurement apparatus comprises anartificial neural network.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the auxiliary measurementdevice connected to the measurement apparatus comprises a localizationdevice.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the machine learnedartificial intelligence module provides the current settings to controlmeasurement functions of said measurement apparatus automatically whenthe measurement apparatus is switched on or is booted up.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the machine learnedartificial intelligence module is adapted to prompt the user via theuser interface of said measurement apparatus about available softwareoptions to perform the current measurement by said measurementapparatus.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the artificialintelligence module is machine learned on the basis of its recordedmeasurement usage history in a separate machine learning process.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the measurement apparatuscomprises a user identification module adapted to identify a user on thebasis of the measurement usage history and/or on the basis of a useridentification input into the user interface of said measurementapparatus or by biometric user identification means of said measurementapparatus.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the artificialintelligence module is learned on the basis of the measurement usagehistory and/or a recorded behaviour of the identified user.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the measurement apparatuscomprises a mobile handheld measurement apparatus for performingmeasurements in the field in an outdoor environment.

In a further possible embodiment of the measurement apparatus accordingto the first aspect of the present invention, the measurement apparatuscomprises a stationary measurement apparatus for performing measurementsin an indoor environment.

The invention further provides according to a further aspect ameasurement system comprising

-   -   at least one measurement apparatus having    -   at least one device interface adapted to connect an auxiliary        measurement device and/or a device under test to said        measurement apparatus,    -   a user interface adapted to input by a user settings for        performing a measurement by said measurement apparatus and an        artificial intelligence module adapted to provide current        settings of said measurement apparatus, wherein said artificial        intelligence module of said measurement apparatus is machine        learned on the basis of connected devices and/or settings during        historic measurements performed by said measurement apparatus,    -   wherein said measurement system further comprises a database        adapted to store the measurement usage history of the        measurement apparatus.

The invention further provides according to a further aspect a methodfor performing a configuration of a measurement apparatus comprising thesteps of:

-   -   recording a measurement usage history of said measurement        apparatus,    -   machine learning an artificial intelligence module of said        measurement apparatus on the basis of the measurement usage        history of said measurement apparatus and    -   generating automatically settings of said measurement apparatus        by said machine learned artificial intelligence module when the        measurement apparatus is activated.

In a possible embodiment of the method according to the third aspect ofthe present invention, the measurement usage history including devicesconnected to said measurement apparatus and settings of measurementsperformed by said measurement apparatus is recorded in a local memory ofsaid measurement apparatus and/or in a remote database connectable tothe measurement apparatus.

BRIEF DESCRIPTION OF FIGURES

In the following, possible embodiments of the different aspects aredescribed in more detail with reference to the enclosed figures.

FIG. 1 shows a block diagram of a possible exemplary embodiment of ameasurement apparatus according to the first aspect of the presentinvention;

FIG. 2 shows a block diagram of a further possible exemplary embodimentof a measurement apparatus according to the first aspect of the presentinvention;

FIG. 3 shows a schematic diagram for illustrating a possible embodimentof a measurement system according to a further aspect of the presentinvention;

FIG. 4 shows a flowchart of a possible exemplary embodiment of a methodaccording to a further aspect of the present invention;

FIG. 5 shows a further flowchart for illustrating a possible exemplaryembodiment of a method according to the present invention;

FIG. 6 shows an exemplary embodiment of a measurement apparatusaccording to the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

As can be seen from the block diagram of FIG. 1, the measurementapparatus 1 according to the present invention comprises in theillustrated embodiment at least one device interface 2 adapted toconnect one or more auxiliary measurement devices and/or devices undertest DUT to said measurement apparatus 1.

In the illustrated embodiment, the measurement apparatus 1 comprisesdevice interfaces 2-1, 2-2 . . . 2-n. The number n of the deviceinterfaces 2-i can vary depending on the type of the respectivemeasurement apparatus 1. The device interfaces 2-i can compriseinterfaces for auxiliary or peripheral devices and device interfaces 2-cfor one or more devices under test 7. The auxiliary measurement devicecan for instance comprise a localization device adapted to localize themeasurement apparatus 1 in the field. The localization device can forinstance comprise a GPS receiver providing coordinates of themeasurement apparatus 1.

The auxiliary measurement device can further comprise a sensor deviceadapted to provide sensor data to the measurement apparatus 1. Themeasurement apparatus 1 as illustrated in FIG. 1 can be a mobilehandheld measurement apparatus for performing measurements in the fieldin an outdoor environment. Alternatively, the measurement apparatus 1can also comprise a stationary measurement apparatus 1 for performingmeasurements in an indoor environment.

The measurement apparatus 1 comprises besides the device interfaces 2-ia user interface 3 adapted to input user settings for performing ameasurement by said measurement apparatus 1. The user interface 3 cancomprise a graphical user interface GUT comprising a screen or displayadapted to output measurement results to a user. The user input 3 canalso comprise a touchscreen adapted to input current user settings forperforming measurements. The user interface 3 can be integrated in themeasurement apparatus 1 as illustrated in the embodiment of FIG. 1.Alternatively, the user interface 3 can form an auxiliary measurementdevice connected via a device interface 2-i to the measurement apparatus1. The user interface 3 is adapted to input settings by a user whereinthe settings are used for performing a measurement by the measurementapparatus 1, for instance in relation to a device under test DUT.

The measurement apparatus 1 comprises an artificial intelligence module4 adapted to provide current settings of the measurement apparatus 1.The artificial intelligence module 4 is machine learned on the basis ofconnected devices and/or settings during historic measurements performedby said measurement apparatus 1. In the illustrated embodiment of FIG.1, the measurement apparatus 1 comprises a local memory 5. Themeasurement usage history of the measurement apparatus 1 can be recordedin the local memory 5 of the measurement apparatus 1. In an alternativeembodiment, the measurement usage history can also be recorded in aremote database 13 to which the measurement apparatus 1 has access asalso illustrated in FIG. 3. The measurement usage history including theconnected auxiliary measurement devices and/or connected devices undertest 7 can be recorded in the local memory 5 of the measurementapparatus 1 and/or in the remote database 13 of the system. In apossible embodiment, the local memory 5 is integrated in the measurementapparatus 1 as shown in FIG. 1. In a possible embodiment, the localmemory 5 can also be connected to the measurement apparatus 1 via adevice interface 2. The local memory 5 can for instance comprise a datacarrier such as a memory stick connectable to the measurement apparatus1 via a device interface 2. The artificial intelligence module 4 islearned on the basis of the measurement usage history in a machinelearning process. In a possible embodiment, the artificial intelligencemodule 4 can be pretrained in a training phase to get an initial settingand then further machine learned during its operation lifetime usingdata recorded in the memory 5. In the illustrated embodiment of FIG. 1,the artificial intelligence module 4 provides an output applied to aninternal control unit 6 of the measurement apparatus 1 which controlsthe internal measurement functions of the measurement apparatus 1 inresponse to the output data provided by the artificial intelligencemodule 4.

The settings input by a user via the user interface 3 can comprisemeasurement parameter settings and/or measurement mode settings. Themeasurement parameter settings are used to adjust measurement parametersrelated to a current measurement setup. The measurement mode settingscomprise different measurement modes and/or operation modes used by themeasurement apparatus 1 to perform a measurement. In a possibleembodiment, the machine learned artificial intelligence module 4provides current settings to control measurement functions of themeasurement apparatus 1 automatically when the measurement apparatus 1is switched on or is booted up. In a possible embodiment, the userinterface 3 comprises a switch which has a press button which can beused by the user to switch on the measurement apparatus 1. When themeasurement apparatus 1 is activated by the user the trained or machinelearned artificial intelligence module 4 can provide current settings tocontrol internally measurement functions of the measurement apparatus 1.In a possible embodiment, the machine learned artificial intelligencemodule 4 is also adapted to prompt the user via the user interface 3 ofthe measurement apparatus 1 about available software options to performa current measurement of the measurement apparatus 1. The artificialintelligence module 4 is learned on the basis of the measurement usagehistory and/or a recorded behavior of an identified user operating themeasurement apparatus 1.

The artificial intelligence module 4 may use algorithms to parse dataand to learn from said parsed data. The artificial intelligence module 4then applies what it has learned to make informed decisions. Theartificial intelligence module 4 can implement an algorithm to parse thedata that was generated when a technician or user was previously usingthe same measurement apparatus 1. The artificial intelligence module 4can learn frequently used settings, frequently used modes, and/orfrequently pressed user interface elements such as pressed buttons, etc.The artificial intelligence module 4 can recommend from the machinelearning process to the user, for instance which page to open once theapparatus 1 is booted up or once a specific button or user interfaceelement has been pressed by the user. For instance, if a user is alwaysusing a Smith chart when operating the measurement apparatus 1, the nexttime the measurement apparatus 1 boots up a machine learning algorithmimplemented in the artificial intelligence module 4 will boot up themeasurement apparatus 1 in a Smith chart operation mode, since it haslearned that this was the mode frequently used by that technician. Othersettings may remain set at default. While machine learning can be usedto provide algorithms that parse, learn and apply what they had learned,deep learning can be used to structure these algorithms in layers tocreate an artificial neural network. The artificial intelligence module4 comprises in a preferred embodiment at least one artificial neuralnetwork that can learn and make intelligent decisions on its own. Inthis embodiment, the deep learning artificial neural network does notjust recommend a correct page once the measurement apparatus 1 boots upor once a specific button has been pressed but it can instead fill upthe settings with values that it determines as being correct in thegiven situation. By using a deep learned artificial intelligence module4, for example a user having used always a Smith chart when operatingthe measurement apparatus 1 the next time the same measurement apparatus1 is booted up, the deep learning algorithm executed by the artificialintelligence module 4 of the measurement apparatus 1 does boot up themeasurement apparatus 1 in a Smith chart mode since it has been learnedthat this was the frequently used mode by the user. Further, theartificial intelligence module 4 will also set the most used settingsjust as points, start and stop frequency, markers, etc., i.e. filling upthe current settings with values that the artificial intelligence module4 determines as being correct in the given measurement setup. Theartificial intelligence module 4 can also prompt the user aboutavailable software options that the user may find useful when doingcertain measurements. The artificial intelligence module 4 can adaptdynamically to a user's behaviour by profiling its usage and predictingwhat settings will be used the next time the measurement apparatus 1 ispowered up. In this way, routine work of inputting settings into themeasurement apparatus 1 can be avoided and the required measurement timecan be reduced.

FIG. 2 shows a further possible exemplary embodiment of a measurementapparatus 1 according to the first aspect of the present invention. Themeasurement apparatus 1, in particular a measurement apparatus which isassigned to a specific user performing measurements in the field, can betrained not only on the measurement usage history of the measurementapparatus but also on the recorded measurement behavior of therespective user. Users such as technicians show a user-specific behaviorwhen inputting settings into the measurement apparatus 1. In a possibleembodiment, the measurement apparatus 1 comprises a user identificationmodule adapted to identify a user on the basis of the measurement usagehistory and/or on the basis of a user identification input into the userinterface 3. Further, the measurement apparatus 1 may comprise biometricuser identification means to identify the current user of themeasurement apparatus 1 (e.g. finger print sensor or voice recognition).In the illustrated embodiment of FIG. 2, the artificial intelligencemodule 4 can comprise a first trained artificial neural network 4A and asecond trained artificial neural network 4B. The first artificial neuralnetwork 4A is trained on a measurement usage history of user settingsirrespective of what kind of users have used the measurement apparatus1. The second artificial neural network 4B can be trained on a recordedbehavior of the specific identified user currently operating themeasurement apparatus 1. The output of the two trained artificial neuralnetworks 4A, 4B can be combined (e.g. concatenated) in a possibleembodiment to provide a result applied to the internal control unit 6 ofthe measurement apparatus 1 triggering matching measurement settings ofthe measurement apparatus 1. In the illustrated embodiment of FIG. 2,current measurement settings are adjusted according to the learned usageprofile of the identified user. The artificial intelligence module 4 canpredict what kind of operation mode and/or parameter settings a userrequires when using the measurement apparatus 1. The measurementapparatus 1 can also suggest available software options that can beloaded by the user as to help him in performing data analysis. Eachartificial neural network 4A, 4B can comprise an input layer IL, severalhidden layers HL and an output layer OL providing an output featurevector applied to the internal control unit 6 which performs internalcontrol functions to execute measurements in response to the receivedfeature vector.

FIG. 3 shows a schematic diagram for illustrating a possible exemplaryembodiment of a measurement system according to the present invention.In the illustrated embodiment, the measurement apparatus 1 is connectedvia a device interface 2 to a device under test DUT 7. The device undertest 7 can comprise for instance a printed current board PCB of amachine to be tested. Further, several devices under test 7 can beconnected to the measurement apparatus 1 in parallel. In the illustratedexample, a sensor device 8 can be connected to another device interface2-i of the measurement apparatus 1. The sensor device 8 can for instancecomprise a current probe used to measure an electrical current I flowingwithin the device under test 7. A plurality of different kinds of sensordevices 8 can be connected to the measurement apparatus 1 such asvoltage sensors, temperature sensors etc. In the illustrated example ofFIG. 3, a localization device 9 such as a GPS receiver can be connectedto the measurement apparatus 1 as well. The localization device 9 canprovide localization data indicating a current position of the apparatus1 in the field. In the illustrated setup of FIG. 3, the measurementapparatus 1 is connected via a further device interface 2 to a datanetwork 10 such as the Internet. A backend platform 11 can comprise aweb server 12 having access to a database 13. In a possible embodiment,the measurement usage history of the measurement apparatus 1 can berecorded in the remote database 13 of the system illustrated in FIG. 3.The measurement usage history of the measurement apparatus 1 can be usedto train the artificial intelligence module 4 of the measurementapparatus 1 in the background continuously. The system shown in FIG. 3can be used to generate automatically user setting configurations forthe measurement apparatus 1. The measurement apparatus 1 can comprise aprocessing unit where the artificial intelligence module 4 isimplemented. The processing unit is able to adjust user settings anduser configurations based on information about connected sensor and/orauxiliary measurement devices and/or measured devices under test data incombination with information of the measurement apparatus usage history.The user settings can be saved or memorized after each measurementprocess.

FIG. 4 shows a flowchart of a possible exemplary embodiment of a methodfor performing a configuration of a measurement apparatus 1 such as themeasurement apparatus 1 illustrated in FIGS. 1 to 3.

In a first step S1, a measurement usage history of the respectivemeasurement apparatus 1 can be recorded. The measurement usage historycan be recorded in a local memory 5 of the respective measurementapparatus 1 and/or in a remote database 13 of a backend platform 11. Themeasurement usage history can be stored in a memory area of the database13 associated with a unique measurement apparatus identifier of themeasurement apparatus 1.

The artificial intelligence module 4 of the measurement apparatus 1 ismachine learned in a further step S2 on the basis of the storedmeasurement usage history of the measurement apparatus 1. The machinelearning process can be performed in an initial training phase toprovide an initial setting of the measurement apparatus 1. Further, themachine learning can be performed during the operation of themeasurement apparatus 1 continuously in the background to improve theperformance of the artificial intelligence module 4. The machinelearning can be performed in a supervised or unsupervised manner.

In a further step S3, the settings of the measurement apparatus 1 aregenerated automatically by the machine learned artificial intelligencemodule 4 when the measurement apparatus 1 is activated.

FIG. 5 shows a flowchart of a further possible embodiment of the methodaccording to the present invention. The process is initiated in stepS50. In a first step S51, it is checked whether enough historic data hasbeen collected for the respective measurement apparatus 1. Accordingly,it is checked whether the measurement usage history of the measurementapparatus 1 identified by the measurement apparatus identifier comprisesenough data to train the artificial intelligence module 4 in such a waythat it provides a sufficient performance. If not enough measurementusage history data is available, a process is triggered in step S52 toget more measurement usage history data, e.g. from the remote database13. If enough measurement usage history data is available, it can bechecked in a further step S53, whether peripheral devices have beenconnected to the apparatus 1. Further, it can be identified in step S54,which applications have been used most by the user to performmeasurements in the past. In a further step S55, it can be inquired whatkind of device under test 7 has been connected to the measurementapparatus 1. In a step S56, the artificial intelligence module 4 canprovide automatically current settings of the measurement apparatus 1 onthe basis of the connected devices, i.e. connected peripheral devicesand/or devices under test 7 and/or other settings during historicmeasurements performed by the same measurement apparatus 1. It canfurther launch required user applications for the current measurement.

FIG. 6 shows a front view on the measurement apparatus 1 with a frontpanel user interface 3. In the illustrated example, the measurementapparatus 1 is a handheld spectrum analyzer having a touch-sensitivedisplay area 3A which can be used to show a spectrum to a user. The userinterface 3 further comprises in the illustrated example soft keys 3B aswell as system keys 3C. Further, the user interface 3 can comprise a keypad 3D with function keys and a rotary knob 3E with ENTER function. Thekey pad 3D can include an alphanumeric key pad and a power key to switchon the measurement apparatus 1. The measurement apparatus 1 furthercomprises a housing with a plurality of different kinds of deviceinterfaces such as an RF input 2A, a BNC connector 2B, a platforminterface 2C and USB ports 2D on top of the housing of the measurementapparatus 1. Further, the measurement apparatus 1 can comprise a DCconnector 2E. Other device interfaces 2F include an interface for alocal area network LAN and one or more several USB ports 2F. Thehandheld spectrum analyzer 1 illustrated in FIG. 6 comprises aprocessing unit with an implemented artificial intelligence module 4adapted to provide current settings of the spectrum analyzer 1. Theartificial intelligence module 4 of the spectrum analyzer 1 illustratedin FIG. 6 is machine learned on the basis of connected devices and/orsettings input by a user during historic measurements performed by themeasurement analyzer apparatus 1. The measurement results can be savedautomatically as soon as the measurement has been completed. Themeasurement results or measurement data and/or the associatedmeasurement settings can be transferred to a tablet or PC and can alsobe stored in the remote database 13 of the system. The measurement timeis reduced thanks to the automatic instrument setting performed by thetrained artificial intelligence module 4 of the measurement apparatus 1.As soon as a user presses the power key of the key pad 3D of the userinterface 3, the measurement apparatus 1 is switched on and the trainedmachine learned artificial intelligence module 4 provides an outputapplied to the internal control unit 6 to provide current settings tocontrol measurement functions of the measurement apparatus 1. Further,the artificial intelligence module 4 may launch required userapplications.

1. A measurement apparatus comprising at least one device interfaceadapted to connect an auxiliary measurement device and/or a device undertest, DUT, to said measurement apparatus; a user interface adapted toinput by a user settings for performing a measurement by saidmeasurement apparatus and an artificial intelligence, AI, module adaptedto provide current settings of said measurement apparatus, wherein saidartificial intelligence, AI, module is machine learned on the basis ofconnected devices and/or settings during historic measurements performedby said measurement apparatus.
 2. The measurement apparatus according toclaim 1 wherein a measurement usage history including connected devicesand settings of measurements performed by said measurement apparatus isrecorded over time in a memory.
 3. The measurement apparatus accordingto claim 2 wherein the measurement usage history of said measurementapparatus is recorded in a local memory of said measurement apparatusand/or in a remote database.
 4. The measurement apparatus according toclaim 1 wherein the settings input by the user via said user interfacecomprise measurement parameter settings and/or measurement modesettings.
 5. The measurement apparatus according to claim 1 wherein themachine learned artificial intelligence, AI, module comprises anartificial neural network.
 6. The measurement apparatus according toclaim 1 wherein the auxiliary measurement device comprises alocalization device.
 7. The measurement apparatus according to claim 1wherein the machine learned artificial intelligence, AI, module providesthe current settings to control measurement functions of saidmeasurement apparatus automatically when the measurement apparatus isswitched on or is booted up.
 8. The measurement apparatus according toclaim 1 wherein the machine learned artificial intelligence, AI, moduleis adapted to prompt the user via the user interface of said measurementapparatus about available software options to perform the currentmeasurement by said measurement apparatus.
 9. The measurement apparatusaccording to claim 1 wherein the artificial intelligence, AI, module ismachine learned on the basis of its recorded measurement usage historyin a separate machine learning process.
 10. The measurement apparatusaccording to claim 1 further comprising a user identification moduleadapted to identify a user on the basis of the measurement usage historyand/or on the basis of a user identification input into the userinterface of said measurement apparatus or by biometric useridentification means.
 11. The measurement apparatus according to claim 1wherein the artificial intelligence, AI, module is learned on the basisof the measurement usage history and/or a recorded behavior of theidentified user.
 12. The measurement apparatus according to claim 1wherein the measurement apparatus comprises a mobile handheldmeasurement apparatus for performing measurements in the field in anoutdoor environment or a stationary measurement apparatus for performingmeasurements in an indoor environment.
 13. A measurement systemcomprising at least one measurement apparatus having at least one deviceinterface adapted to connect an auxiliary measurement device and/or adevice under test, DUT, to said measurement apparatus, a user interfaceadapted to input by a user settings for performing a measurement by saidmeasurement apparatus and an artificial intelligence, AI, module adaptedto provide current settings of said measurement apparatus, wherein saidartificial intelligence, AI, module of said measurement apparatus ismachine learned on the basis of connected devices and/or settings duringhistoric measurements performed by said measurement apparatus, whereinsaid measurement system further comprises a database adapted to storethe measurement usage history of the measurement apparatus.
 14. A methodfor performing a configuration of a measurement apparatus comprising thesteps of: recording a measurement usage history of said measurementapparatus, machine learning an artificial intelligence, AI, module ofsaid measurement apparatus on the basis of the measurement usage historyof said measurement apparatus and generating automatically the settingsof said measurement apparatus by said machine learned artificialintelligence, AI, module when the measurement apparatus is activated.15. The method according to claim 14 wherein the measurement usagehistory including devices connected to said measurement apparatus andsettings of measurements performed by said measurement apparatus isrecorded in a local memory of said measurement apparatus and/or in aremote database.